Dissertation Workshop: Exercise 2
Johns Hopkins Bloomberg School of Public Health OpenCourseWare
1. A hospital record review shows:
Abortion Cases
Literate
Illiterate
Total
Number
12
280
400
Percent
30%
70%
100%
A. No relationship between abortion and education
B. Not enough information
C. Abortion is more common among literate women
2. This analysis shows
Urban
Rural
Population
9,266
4,351
Cases
50
33
Cases per Thousand
5.40
7.58
A. Cases are more likely in rural areas
B. Not enough information
C. Cannot compare the populations
3. If the clinical record you are reviewing does not directly ask questions about potentially related
variables, one possible way to solve this problem would be to:
A.
B.
C.
D.
Find each patient and reinterview them
Create a proxy variable
Assume that the data would not be useful
Use other data
4. A bio-social mechanism is a _______________ or ___________________.
5. A series of questions relating symptoms to a particular condition is called a _______________.
6. True or False
A. Diagnostic algorithms are used when you cannot ask directly about an event or condition.
B. Diagnostic algorithms are 100% accurate.
C. Diagnostic algorithms are confirmed by research.
D. Diagnostic algorithms can be used in research to confirm a case of measles.
E. Diagnostic algorithms should not be used when interviewing people.
Selection of Variables and Conceptual Models
I. Terminology and Definitions
There were a number of technical terms introduced in this lecture. It is important that these are
completely understood. Below are listed many of the terms that were introduced. You should review
the lecture and be prepared to define and discuss the meaning of each of these terms.
Dependent Variable
Independent Variable
Intervening/Proximate/Intermediate Variable
Biosocial Mechanisms
Case-Control Study
Matched Control
Relative Risk
Conceptual/Analytical Framework/Model
Diagnostic Algorithms
Validation Study
Summative Scale
Interactions
II. Identifying Variables
A variable is a characteristic of a person, object or phenomenon which is a measurable and, thus, can
take on different values. Variables can be quantitative and measured numerically (for example, age,
height, weight, blood pressure, etc.) Or qualitative and measured in terms of categories for example
sex (male or female), outcome of disease (recovery, chronic illness, disability or death), mode of
transport (foot, bus, car, other). As noted in the lecture, in order to ensure that all data relevant to the
research objectives are collected, variables must be carefully identified. Ordinarily, it is easiest to
derive the variables from a conceptual model or diagram of factors influencing the problem under
investigation.
Tilley B, Barnes A, Bergstrahl E., Labarthe D., Noller K, Colton T, Adam E. 1985. A Comparison of
Pregnancy History Recall and Medical Records. American Journal of Epidemiology 121 (2): 269-281.
World Health Organization. 1993. A Randomized Controlled Trial for the Evaluation of a New
Antenatal Care Model. Geneva: Special Programme in Research, Development, and Research
Training in Human Reproduction and Maternal Health and Safe Motherhood, Division of Family
Health, World Health Organization, pages 4-25.
References
Bongaarts J. 1978. A Framework for Analyzing the Proximate Determinants of Fertility. Population
and Development Review 4:105-132.
Cronback LJ. 1951. Coefficient Alpha and the Internal Structure of Tests. Psychometrika 16:297-334.
Davis K, Blake J. 1956. Social Structure and Fertility: An Analytic Framework. Economic
Development and Cultural Change 4:211-235.
Demographic and Health Surveys. 1994. Ten Years of DHS Surveys. Newsletter 6(2), 1994.
Calverton, MD: DHS, Macro International Inc., 11785 Beltsville Drive, Suite 300, Calverton, MD
20705.
Gray R. 1991. Verbal Autopsy: Using Interview to Determine Causes of Death in Children. Occasional
Paper No. 14. Baltimore, MD: Johns Hopkins University School of Hygiene and Public health, Institute
for International Programs.
Labbok M, Krasovec K. 1990. Toward Consistency in Breastfeeding Definitions. Studies in Family
Planning 21(4): 226-230.
Mavalankar D, Trivedi C, Gray R. 1991. Levels and Risk Factors for Perinatal Mortality in
Ahmedabad, India. Bulletin of the World Health Organization 69 (4): 435-442.
Mosley W., Chen L. 1984. An Analytic Framework for the Study of Child Survival in Developing
Countries. Pages 25-45 in Child Survival: Strategies for Research, W. Mosley and L. Chen (eds).
Population and Development Review, Supplement 10. National Statistics Office. 1993. Philippines
National Safe Motherhood Survey, Manila. Calverton, MD: Macro International, Inc.
Spector P. 1992. Summated Rating Scale Construction, An Introduction, Beverly Hills, London, New
Delhi: Sage Publication #82, p. 32.
Stanton C. 1996. An Investigation into the Use of Demographic Survey Data for the Study of Perinatal
Mortality and Its Determinants. PhD theses, Department of Population Dynamics, Johns Hopkins
University School of Hygiene and Public Health, Baltimore, MD.
Stewart MK, Boerma JT, Van Ginneken JT. 1994. Assessment of the Quality of Data on Maternity
Care in DHS Surveys. Pages 61-77 in Methodological Report #2: An Assessment of the Quality of
Health Data in DHS Surveys. Calverton, MD: Macro International, Inc.
Stewart M. Festin M., 1995. Validation Study of Women's Reporting and Recall of Major Obstetric
Complications Treated at the Philippines General Hospital. International Journal of Obstetrics and
Gynecology 48, Supplement: S53-S66.